For most businesses, it’s much cheaper to retain existing customers than to acquire new customers. Part of that depends on your Customer Acquisition Cost, but a lot of it is due to existing customers being highly qualified.
So, if we don’t want to lose customers we should make them all happy, right?
Sure, but that is harder than it sounds. No product makes everyone equally happy, and there are always new competitors waiting in the wings trying to steal our customers away. It would be great if there was some way to know which customers were most at risk so we could focus on making them happier.
There is and it’s called Churn Prediction.
Churn Prediction works much like Lead Scoring, but instead of estimating the likelihood a lead converts into a customer it estimates the likelihood that a customer will churn. The same sort of company / personal demographic data we discussed yesterday (e.g., company size) are appropriate here. However, the behavioral data will need to be updated to also include information about how the customer has engaged with your product.
- Product usage: How many times has the customer logged in? What is the cadence of their interaction with your product? How many users are there using your product at the customer? Has the customer changed their level of service (upgraded or downgraded)? What is the tenure of your relationship with the customer?
- Support: How many support tickets have been filed by the customer? What was the severity of each ticket? What was the tone of the tickets? How quickly were the tickets resolved?
- Social: Has the customer every mentioned you in social media? Has the customer referred you to any other companies? Are you allowed to use the customer’s logo on your website? Was the customer willing to produce a case study for you to publicize?
With all of these attributes defined and data collected, you’ll be able to score all of your customers and have a good sense as to which ones are likely going to stay customers and which are going to churn.
How do I predict if the customer will churn?
Like Lead Scoring, creating a score for churn does not provide you a prediction as to whether the customer will churn or not. The logistic regression technique I mentioned yesterday is still applicable here, but there are other options as well, such as the class of models called Markov Models. These types of models are state-based, in other words, you define the probability of moving between different states under a key assumption that the probability of moving from one state to another is only dependent on the last state you were in (the Markov assumption). The Hidden Markov Model makes the most sense because, as the name suggests, a customer’s commitment to your product is hidden, i.e., not directly observable. With a model of this type, you’ll be able to understand the paths customers take to renewal and churn.
As we discussed yesterday, with any modeling effort, data quality will strongly influence your results and you will want to continually evaluate the attributes and parameters of your model to better predict your outcomes in the future.
You’ll likely use tools to help you with Churn Prediction; I hope this post is helpful to get you more familiar with the techniques these tools rely on so you are better informed when making your choice.
Quote of the Day: “We see our customers as invited guests to a party, and we are the hosts. It’s our job every day to make every important aspect of the customer experience a little bit better.” Jeff Bezos